Harnessing the Power of Big Data to Improve Drug R&D

Chris Moore

September 15, 2016

4 Min Read

Like many other industries, biotechnology is being transformed by the emergence of big data — extremely large data sets that can be analyzed to reveal patterns, trends, and associations — and advanced analytics. Information from multiple sources such as electronic health records, payer claims, and mobile health platforms is growing exponentially. When used and harnessed properly, these data can boost the efficiency of drug research and development (R&D) in three critical areas: early R&D investment, drug development, and personalized medicine.

Boosting Efficiencies
First, big data can help companies understand the research landscape and make smart, early stage investments. Increasingly, research activity is sourced externally from other companies and academic institutions. Consequently, accurate identification of external research of interest gives a company a competitive advantage in R&D and partnering. Tools such as data mining and automated learning/searches can allow companies to pinpoint important players and get ahead of their competition by buying into promising research before it gets too expensive.

Second, big data can enable drug makers to make better drugs by deciphering the biological derivation of disease or the mode of action for a given drug. This can involve identifying a pathway to target or understanding the likely effects of a certain treatment, and it can help in developability decision making. For example, the true benefits of chronic disease drugs such as statins take decades to play out. Running a clinical trial to measure associated endpoints would be lengthy and expensive. But analytics platforms help companies overcome such obstacles by enabling systems biology and crowdsourcing to verify models and measure the likely long-term impacts of a given product.

Finally, big data can help match the right drugs to the right patients. Data mining gives companies the ability to analyze combinations of variables such as gender, ethnicity, and disease history to identify which patient cohorts will be most responsive to specific drugs and treatment regimes. In today’s outcomes-focused environment, such analyses can provide strong financial benefits. By analyzing how a drug works and in which patients it will be most effective, a company can identify additional how to boost a given product’s impact and therefore support its case for reimbursement.

Access to Data
Despite all those benefits, big data can have a downside. One unfortunate reality is that it is not yet fully democratized: Gaining access remains expensive. A biotech company could easily spend several million dollars just to collect and manage information — which represents a daunting barrier for small, resource-constrained organizations.

In addition, large amounts of data generate insights only when paired with robust analytics. Today, sophisticated algorithms and systems can analyze many more dimensions of data than would have been feasible even a few years ago. However, such processing capabilities are not inexpensive, so they are typically beyond the in-house capabilities of smaller companies. And although high-end, cloud-based data analytics are becoming available, access to such capabilities has not yet filtered down to all organizations.

The good news is that as the volume of data grows, and more entities create new analytical programs, gaining access to those robust data analytics will become easier and less expensive. Also, information does not have to come only from commercial organizations looking to sell it for a profit. Disease foundations, government agencies, and other groups will work with life-sciences companies focused on areas that align with their missions.

Precompetitive collaborations and other consortia can play an important role as well. Such joint efforts, typically created to address a shared scientific or methodological challenge, often pool data and devise mechanisms to share information openly with their members. Participating in such efforts gives emerging biotech companies access to data they might not otherwise be able to afford.

The Challenge
Transforming big data into actionable information is a collective challenge, one that will require the strengths and capabilities of a diverse set of players. Information held by a consortium may need to be supplemented with analytical capabilities that are becoming available in platform mode. Funding for such efforts will typically come from big players. But what might fuse it all together is the creativity and fresh thinking of entrepreneurial, dynamic biotechnology companies.

The data revolution is transforming drug R&D by helping life-sciences companies enter into smarter research partnerships and develop better drugs than ever before — and match those drugs to the patients who will most benefit from them. In the future, the most successful biotechnology companies will be those that can harness the full potential of data analytics and use them to create a competitive advantage.

Chris Moore is a partner with the Life Sciences Advisory at EY (formerly Ernst & Young); 1 More London Place, London, SE1 2AF United Kingdom; 44-20-7951-2000; [email protected].  

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